🛠️ Review Campaign

Focus Question: What decisions and have I already made about my project?

1 Campaign Overview

Title: Multi Objective mixed categorical and numeric inputs (testing)
Description: This is an anonimized dataset for testing purposes
Version Information: Initial version.
Number of Suggestions: 5

2 Input Variable Settings

Categorical, Discrete, or Time Levels

(Input Variable Categories & Levels)

Variable Name	Variable Type	Number of Levels	Levels
input_2	Categorical	6	DMAc, DMF, DMPU, DMSO, NMP, Propionitrile
input_5	Categorical	4	DPPP, L29 | DPPF, L33 | XantPhos, L59 | N-XantPhos
input_7	Categorical	2	(TMS)3SiH, PMHS

Table 1: Table showing categorical, discrete, or time-based input variables and their levels.

Continuous Configuration

(Continuous Variable Ranges & Steps)

	input_1	input_3	input_4	input_6	input_8
Min	90.0	6.00	0.5	0.015	0.10
Max	120.0	10.00	1.0	0.042	0.60
Step	1.0	0.01	0.1	0.001	0.01

Table 2: Table displaying the ranges and step sizes for continuous input variables.

3 Output Variable Settings

Output Configuration

(Optimization Objectives, Bounds, & Weights)

	Output	Objective	Weight	Bounds	Track
0	output_1	maximize	0.5	(0.0, 100.0)	False
1	output_2	maximize	0.5	(0.0, 100.0)	False

Table 3: Table outlining the objectives, bounds, and weights for output variables.

4 Model

Model Configuration

(Model Type, Hyperparameters, and Settings)

	Model	Tuning Strategy	Hyperparameters
output_1	GP	TuningStrategy.ADVANCED	Smoothness: 1.5, Learning Rate: 0.1, Training Iterations: 200, Mean Function: constant, Kernel: rbflinear
output_2	GP	TuningStrategy.ADVANCED	Smoothness: 1.5, Learning Rate: 0.1, Training Iterations: 200, Mean Function: constant, Kernel: matern

Table 4: Table summarizing the model type, hyperparameters, and settings used in the campaign.